Farhad is a researcher in VisTeam and a Phd student at Instituto Universitário de Lisboa and Faculty of Science of the University of Lisbon. He is researching in area of Natural Language processing and deep learning to modelling News and Tweets for the market. He studied advance statistical physics in bachelor and in master, He studied complex system at department of Physics.
Projects
UniqueMark
This project aims to improve the safety of INCM's contrasting marks in precious metal artefacts (the...
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FACING
Os principais objetivos deste projeto são a realização de baterias exaustivas de testes de ferrament...
VISUAL-ID – Unique Visual Identities in Graphics, Images and Faces
The Visual-ID project emerge in the context of the partnership between the Imprensa Nacional-Casa da...
TruIM – Trust Image Understanding
TruIm Project aims at developing technologies to authenticate objects in certified images, encoded u...
Publications
Towards Facial Biometrics for ID Document Validation in Mobile Devices
Various modern security systems follow atendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.
- Date: 01/07/2021
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- Featured In: Applied Sciences
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- Publication Type: Journal Articles
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- Author(s): Iurii Medvedev, Farhad Shadmand, Leandro Cruz, Nuno Gonçalves
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- DOI: 10.3390/app11136134
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CodeFace: a deep learning printer-proof steganography for Face Portraits.
Identity Documents (IDs) containing a facial portrait constitute a prominent form of personal identification. Photograph substitution in official documents (a genuine photo replaced by a non- genuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still efficient ways of misleading the national authorities in in-person identification processes. Therefore, in order to confirm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper. The encoded face image looks like the original image to a naked eye. Our architecture is called CodeFace. CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras. The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images. Extensive experiments were performed with real printed documents and smartphone cameras. The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits.
- Date: 29/10/2021
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- Featured In: IEEE Access
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- Publication Type: Journal Articles
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- Author(s): F. Shadmand, I. Medvedev and N. Gonçalves
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- DOI: 10.1109/ACCESS.2021.3132581
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MorDeephy: Face Morphing Detection Via Fused Classification (preprint)
Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a so- phisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these fea- tures. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a promi- nent ability for generalising the task of morphing detection to unseen scenarios.
- Date: 05/08/2022
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- Featured In: arXiv
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- Publication Type: Journal Articles
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- Author(s): Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves
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- DOI: 10.48550/arXiv.2208.03110
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MorDeephy: Face Morphing Detection via Fused Classification
Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a sophisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalizing the task of morphing detection to unseen scenarios.
- Date: 22/02/2023
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- Featured In: 12th International Conference on Pattern Recognition Application and Methods (ICPRAM), Lisbon, Portugal.
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev, Farhad Shadmand and Nuno Gonçalves
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Steganography Applications of StyleGAN: A (…) Investigation from Hiding Message in Face Images
In this investigation, we delve into the latent codes denoted as w, pertaining to both original and encoded images in steganography models, which are projected through StyleGAN—a generative adversarial network renowned for generating aesthetic synthesis. We present evidence of disentanglement and latent code alterations between the original and encoded images. This investigator possesses the potential to assist in the concealment of messages within images through the manipulation of latent codes within the original images, resulting in the generation of encoded images. The message into encoded renderings is facilitated by the employment of CodeFace, serving as a steganography model. CodeFace comprises an encoder and decoder architecture wherein the encoder conceals a message within an image, while the decoder retrieves the message from the encoded image. By gauging the average disparities amid the latent codes belonging to the original and encoded images, a discerning revelation of optimal channels for concealing information comes to light. Precisely orchestrated manipulation of these channels furnishes us with the means to engender novel encoded visual compositions.
- Date: 27/10/2023
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- Featured In: RECPAD - 29th Portuguese Conference on Pattern Recognition. Coimbra (2023), Portugal
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- Publication Type: Poster
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- Author(s): Farhad Shadmand, Luiz Schirmer and Nuno Gonçalves
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